DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs

Challenge:	Existing approaches to answer questions over Knowledge Graphs (KGQA) are not available for KGQA.
Approach:	They propose a framework to improve the neural-symbolic reasoning capabilities of language agents powered by Large Language Models (LLMs) they show that DARA can be efficiently trained with a small number of high-quality reasoning trajectories.
Outcome:	The proposed framework outperforms in-context learning-based agents with GPT-4 and alternative fine-tuned agents across different benchmarks.

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